
Random assignment: Dispatch points are selected at random.

Double Deep Q Network (ddqn): Standard Deep Q Network, with policy and target networks [1]}.

Duelling Deep Q Network (3dqn): Policy and target networks calculate Q from sum of *value* of state and *advantage* of each action (*advantage* represents the added value of an action compared to the mean value of all actions) [2]}.

Noisy Duelling Deep Q Network (noisy 3dqn). Networks have layers that add Gaussian noise to aid exploration [3]}.

Prioritised Replay Duelling Deep Q Network (pr 3dqn). When training the policy network, steps are sampled from the memory using a method that prioritises steps where the network had the greatest error in predicting Q [3]}.

Prioritised Replay Noisy Duelling Deep Q Network (pr noisy 3dqn). Combining prioritised replay with noisy layers.

Bagging Deep Q Network (bagging ddqn), with 5 networks. Multiple networks are trained from different bootstrap samples from the memory [5]}. Action may be sampled at random from networks, or a majority vote used.

Bagging Duelling Deep Q Network (bagging 3dqn), with 5 networks. Combining the bagging multi-network approach with the duelling architecture.

Bagging Noisy Duelling Deep Q Network (bagging noisy 3dqn) with 5 networks. Combining the bagging multi-network approach with the duelling architecture and noisy layers.

Bagging Prioritised Replay Noisy Duelling Deep Q Network (bagging pr noisy 3dqn) with 5 networks. Combining the bagging multi-network approach with the duelling architecture, noisy layers, and prioritised replay.

